Argentina

Data on Education Hackathon

The Hackathon Data on Education was organized by the observatory Argentinians for Education (Span. Argentinos por la Educación) and had two editions, one in 2020 and a second one in 2021. In the events, citizens were invited to think of solutions for the education sector based on data science. Thereby, the projects are intended to use existing data for decision making. The winning project in 2020, selected by a jury of leaders in the field of education, measures the levels of grade repetition and over-age in both primary and secondary schools and analyzes how this relates to current public policies.

Institutional design

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Formalization: is the innovation embedded in the constitution or legislation, in an administrative act, or not formalized at all?

Frequency: how often does the innovation take place: only once, sporadically, or is it permanent or regular?

Mode of Selection of Participants: is the innovation open to all participants, access is restricted to some kind of condition, or both methods apply?

Type of participants: those who participate are individual citizens, civil society organizations, private stakeholders or a combination of those?

Decisiveness: does the innovation takes binding, non-binding or no decision at all?

Co-governance: is there involvement of the government in the process or not?

Formalization
not backed by constitution nor legislation, nor by any governmental policy or program 
Frequency
single
Mode of selection of participants
open 
Type of participants
citizens  
Decisiveness
democratic innovation yields a non-binding decision  
Co-Governance
no 

Means


  • Deliberation
  • Direct Voting
  • E-Participation
  • Citizen Representation

Ends


  • Accountability
  • Responsiveness
  • Rule of Law
  • Political Inclusion
  • Social Equality

Policy cycle

Agenda setting
Formulation and decision-making
Implementation
Policy Evaluation

Sources

How to quote

Do you want to use the data from this website? Here’s how to cite:

Pogrebinschi, Thamy. (2017). LATINNO Dataset. Berlin: WZB.

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